Advances in Geospatial Technology for Early Detection of Mango Leaf Diseases

Mango leaves are susceptible to pests and pathogens including anthracnose, powdery mildew, diebacks, bacterial infection which cause serious loss of mango production, quality and sustainability. Recent developments in geospatial technology have provided new opportunities for the early, accurate, and large-scale detection of disease. This article offers a comprehensive review of methods, datasets and tools for monitoring mango leaf diseases through remote sensing and geoscience. High-resolution UAV images, multispectral and hyperspectral satellite data (e.g. Sentinel-3) along with field level spectral information serve as the basic sources of data for disease detection. There is a need for preprocessing steps such as resizing, denoising and normalization to enhance the reliability of models. The feature extraction with the help of vegetation indices, Band Ratios and texture metric GLCM increases the discrimination capacity between healthy and infected leaf. SVM, Random Forest, CNNs, and hybrid models of machine learning and deep learning have demonstrated high performance in classification when trained on a well-processed dataset. A key advance presented in this review is the development of portable spectrometers, which provide very accurate leaf-level reflectance profiles and become the crucial ground truth for calibrating geospatial disease detection models. The combined use of spectrometer data with UAV and satellite imagery significantly enhances disease identification sensitivity, especially at early stages of disease infection when the visual symptoms are minimal. In general, this review confirms that combining geospatial technologies, spectroscopy, and smart classification frameworks will provide an efficient, accurate, and scalable solution for mango disease monitoring to support precision horticulture and sustainable orchard management.

• Publication year

  2026

• Venue

  International Journal on Advanced Computer Theory and Engineering

• Publication date

  2026-01-18

• Fields of study

  Not labeled

• Identifiers
  DOI 10.65521/ijacte.v15i1s.1329
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• ViX-MangoEFormer: An Enhanced Vision Transformer-EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence

  2025cited by this paper
• High-Precision Mango Orchard Mapping Using a Deep Learning Pipeline Leveraging Object Detection and Segmentation

  2024influential reference
• Cybersecurity Risk Prediction Using Graph Neural Networks

  2024cited by this paper
• An Effective Stage of Preprocessing Design for Identifying Mango Leaf Disease via Hybrid Filtration

  2024cited by this paper
• Enhancing mango disease diagnosis through eco-informatics: A deep learning approach

  2023cited by this paper
• A Hybrid Filtration Based Design of Pre-Processing Stage for Efficient Mango Leaf Disease Detection

  2023cited by this paper
• Enhancing Mango Leaf Disease Classification: ViT, BiT, and CNN-Based Models Evaluated on CycleGAN-Augmented Data

  2023cited by this paper
• A Systematic Analysis of Various Techniques for Mango Leaf Disease Detection

  2023cited by this paper
• Integrating Remote Sensing and Weather Variables for Mango Yield Prediction Using a Machine Learning Approach

  2023cited by this paper
• Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango (Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe

  2022cited by this paper
• MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves

  2022cited by this paper
• Nutritional Composition and Bioactive Compounds in Three Different Parts of Mango Fruit

  2021cited by this paper
• Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

  2021cited by this paper
• Deficit irrigation strategies for subtropical mango farming. A review

  2021cited by this paper
• The use of Landsat-8 and Sentinel-2 imageries in detecting and mapping rubber trees

  2021cited by this paper
• Machine Learning Algorithm Development for detection of Mango infected by Anthracnose Disease

  2021cited by this paper
• Mango leaf disease recognition using neural network and support vector machine

  2020cited by this paper
• Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica)

  2020cited by this paper
• A Novel Fusion of Deep Learning and Android Application for Real-Time Mango Fruits Disease Detection

  2020cited by this paper
• Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection

  2020cited by this paper
• Detection and Classification of Diseased Mangoes

  2020cited by this paper
• Data augmentation for automated pest classification in Mango farms

  2020cited by this paper
• Mango

  2013cited by this paper

• No citing papers are available for this paper.